报 告 人：张松 教授
Song Zhang is an assistant head for Experiential Learning and professor of mechanical engineering and at Purdue University. His primary research focuses on high-speed 3D optical sensing/imaging and optical information processing. He has over 200 publications including 120 journal articles and 2 books. He has received awards including AIAA Best Paper Award, IEEE ROBIO Best Conference Paper Award, Best of SIGGRAPH Disney Emerging Technologies Award, NSF CAREER Award, Stony Brook University’s inaugural “Forty under 40 Alumni Award”, as well as the CoE Early Career Faculty Research Excellence Award from Purdue and ISU. He is currently serving as an associate editor for Journal of Optics and Lasers in Engineering, and as a technical editor for IEEE/ASME Transactions on Mechatronics. He is a fellow of SPIE and OSA.
Advances in optical imaging and machine/computer vision have provided integrated smart sensing systems for the manufacturing industry; and advanced 3D imaging could have profound impact on numerous fields, with broader applications including manufacturing, biomedical engineering, and entertainment. Our research addresses the challenges in high-speed, high-resolution 3D imaging and optical information processing.Our current research focuses on achieving speed breakthroughs by developing the binary defocusing techniques; and exploring novel means to store enormously large 3D geometric data by innovating geometry/video compression methods. The novel methods of converting 3D data to regular 2D counterparts offer us the opportunity to leverage mature 2D data compression platform, achieving extremely high compression ratios without reinventing the whole data compression infrastructure. In this talk, I will present two platform technologies: 1) superfast 3D optical imaging; and 2) real-time 3D video communication. I will also cover some of the applications that we have been exploring including mechanics, robotics, forensics, and entertainment.